MRI Gradient Coil Non-Linearity Correction via Voxel Tensor
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Solution Overview
Problem
Gradient coil non-linearities in magnetic resonance imaging (MRI) systems cause errors in spatial encoding, particularly pronounced in asymmetric coils, leading to spatially dependent errors in motion-sensitive imaging, which are difficult to correct using conventional experimental or numerical methods.
Innovation Solution
A method and system that utilize a computer model of the gradient coils to obtain a non-linearity tensor at each voxel within the imaging space, correct motion-sensitive encoding, and generate a corrected image using the non-linearity tensor, allowing for accurate representation and correction of gradient field non-linearities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If spherical harmonic expansion is used to calculate the gradient field, then the field can be represented analytically, but the number of harmonic terms grows substantially for asymmetric gradient coils making accurate representation difficult
Solution Approach 1:
The patent changes the mathematical parameters used to represent the gradient field from spherical harmonic expansion to a direct coordinate-based calculation method. By computing the gradient field using the actual coil geometry and position vectors rather than harmonic terms, the representation becomes both simpler and more accurate for asymmetric coils.
2Measurement precision
If numerical calculation of gradient is performed using higher resolution sampling, then the gradient can be determined more accurately, but the processing time increases
Solution Approach 1:
The patent performs preliminary calculation of the non-linearity tensor using the exact gradient field expressions derived from coil geometry. By pre-computing the tensor components analytically rather than numerically sampling during image reconstruction, the method achieves high accuracy without increasing processing time during the actual imaging process.
3Measurement precision
If experimental methods are used to find the magnetic field of gradient coils, then the field can be measured directly, but voxel discretization leads to discrete distortion maps rather than continuous ones and B0 inhomogeneity errors are included
Solution Approach 1:
The patent replaces the experimental mechanical measurement system with an analytical calculation system based on electromagnetic theory. By using the known coil geometries and applying Biot-Savart law or similar electromagnetic principles, the method computes the continuous magnetic field and gradient analytically, avoiding discretization errors and separation of B0 inhomogeneity effects.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively corrects errors caused by gradient coil non-linearities, improving the accuracy of MRI images by providing a precise non-linearity tensor for each voxel, enabling better spatial encoding and reducing processing time, especially for asymmetric coils used in stroke imaging.
Implementation Method 1
gradient coils (high power electromagnets) may be used to encode spatial information. The spatial encoding is achieved by causing the gradient coils to produce a linearly varying magnetic field with position
Implementation Method 2
obtain a non-linearity tensor at each voxel within the imaging space using a computer model of the gradient coil
Data Source
AI summary
The present disclosure provides a method and system for correcting errors caused by non-linearities in a gradient field profile of a gradient coil in a magnetic resonance imaging (MRI) system. The method includes obtaining a non-linearity tensor at each voxel within the imaging space using a computer model of the gradient coil; correcting motion sensitive encoding using the non-linearity tensor; and generating a corrected image using the corrected motion sensitive encoding.